{
 "metadata": {
  "name": "Gauss_Markov"
 },
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   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Introduction\n",
      "-------------------\n",
      "\n",
      "In this section, we  consider the famous Gauss-Markov problem which will give us an opportunity to use all the material we have so far developed. The Gauss-Markov is the fundamental model for noisy parameter estimation because it estimates unobservable parameters given a noisy indirect measurement.  Incarnations of the same model appear in all studies of Gaussian models. This case is an excellent opportunity to use everything we have so far learned about projection and conditional expectation."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Following Luenberger (1997), let's  consider the following problem:\n",
      "\n",
      "$$ \\mathbf{y} = \\mathbf{W} \\boldsymbol{\\beta} + \\boldsymbol{\\epsilon} $$\n",
      "\n",
      "where $\\mathbf{W}$ is a $ n \\times m $ matrix, and $\\mathbf{y}$ is a $n \\times 1$ vector. Also, $\\boldsymbol{\\epsilon}$ is a $n$-dimensional random vector with zero-mean and covariance\n",
      "\n",
      "$$ \\mathbb{E}( \\boldsymbol{\\epsilon} \\boldsymbol{\\epsilon}^T) = \\mathbf{Q}$$\n",
      "\n",
      "Note that real systems usually provide a *calibration mode* where you can estimate $\\mathbf{Q}$ so it's not fantastical to assume you have some knowledge of the noise statistics. The problem is to find a matrix $\\mathbf{K}$ so that $ \\boldsymbol{\\hat{\\beta}} = \\mathbf{K} \\mathbf{y}$  approximates $ \\boldsymbol{\\beta}$.  Note that we only have knowledge of $\\boldsymbol{\\beta}$ via $ \\mathbf{y}$ so we can't measure it directly. Further, note that $\\mathbf{K} $ is a matrix, not a vector, so there are $m \\times n$ entries to compute. \n",
      "\n",
      "We can approach this problem the usual way by trying to solve the MMSE problem:\n",
      "\n",
      "$$ \\min_K \\mathbb{E}(|| \\boldsymbol{\\hat{\\beta}}- \\boldsymbol{\\beta} ||^2)$$\n",
      "\n",
      "which we can write out as\n",
      "\n",
      "$$ \\min_K \\mathbb{E}(|| \\boldsymbol{\\hat{\\beta}}- \\boldsymbol{\\beta} ||^2)\n",
      "=  \\min_K\\mathbb{E}(|| \\mathbf{K}\\mathbf{y}- \\boldsymbol{\\beta} ||^2)\n",
      "=  \\min_K\\mathbb{E}(|| \\mathbf{K}\\mathbf{W}\\mathbf{\\boldsymbol{\\beta}}+\\mathbf{K}\\boldsymbol{\\epsilon}- \\boldsymbol{\\beta} ||^2)$$\n",
      "\n",
      "and since $\\boldsymbol{\\epsilon}$ is the only random variable here, this simplifies to\n",
      "\n",
      "$$\\min_K || \\mathbf{K}\\mathbf{W}\\mathbf{\\boldsymbol{\\beta}}- \\boldsymbol{\\beta} ||^2 + \\mathbb{E}(||\\mathbf{K}\\boldsymbol{\\epsilon} ||^2)\n",
      "$$\n",
      "\n",
      "The next step is to compute\n",
      "\n",
      "$\\DeclareMathOperator{\\Tr}{Trace}$\n",
      "$$ \\mathbb{E}(||\\mathbf{K}\\boldsymbol{\\epsilon} ||^2) = \\mathbb{E}(\\boldsymbol{\\epsilon}^T \\mathbf{K}^T \\mathbf{K}^T \\boldsymbol{\\epsilon})=\\Tr(\\mathbf{K \\mathbb{E}(\\boldsymbol{\\epsilon}\\boldsymbol{\\epsilon}^T) K}^T)=\\Tr(\\mathbf{K Q K}^T)$$\n",
      "\n",
      "using the properties of the trace of  a matrix. We can assemble everything as\n",
      "\n",
      "$$  \\min_K || \\mathbf{K W} \\boldsymbol{\\beta} -  \\boldsymbol{\\beta}||^2 + \\Tr(\\mathbf{K Q K}^T) $$\n",
      "\n",
      "Now, if we were to solve this for $\\mathbf{K}$, it would be a function of $ \\boldsymbol{\\beta}$, which is the same thing as saying that the estimator, $ \\boldsymbol{\\hat{\\beta}}$, is a function of what we are trying to estimate, $ \\boldsymbol{\\beta}$, which makes no sense. However, writing this out tells us that if we had $\\mathbf{K W}= \\mathbf{I}$, then the first term  vanishes and the problem simplifies to\n",
      "\n",
      "$$ \\min_K \\Tr(\\mathbf{K Q K}^T) $$\n",
      "\n",
      "with\n",
      "\n",
      "$$ \\mathbf{KW} = \\mathbf{I}$$\n",
      "\n",
      "This requirement is the same as asserting that the estimator is unbiased,\n",
      "\n",
      "$$ \\mathbb{E}( \\boldsymbol{\\hat{\\beta}}) = \\mathbf{KW}  \\boldsymbol{\\beta} =  \\boldsymbol{\\beta}  $$ \n",
      "\n",
      "To line this problem up with our earlier work, let's consider  the $i^{th}$ column of $\\mathbf{K}$, $\\mathbf{k}_i$. Now, we can re-write the problem as\n",
      "\n",
      "$$ \\min_k (\\mathbf{k}_i^T \\mathbf{Q} \\mathbf{k}_i) $$\n",
      "\n",
      "with\n",
      "\n",
      "$$ \\mathbf{k}_i^T \\mathbf{W} = \\mathbf{e}_i$$\n",
      "\n",
      "and from our previous work on contrained optimization, we know the solution to this:\n",
      "\n",
      "$$ \\mathbf{k}_i  = \\mathbf{Q}^{-1} \\mathbf{W}(\\mathbf{W}^T \\mathbf{Q^{-1} W})^{-1}\\mathbf{e}_i$$\n",
      "\n",
      "Now all we have to do is stack these together for the general solution:\n",
      "\n",
      "$$ \\mathbf{K}  = (\\mathbf{W}^T \\mathbf{Q^{-1} W})^{-1} \\mathbf{W}^T\\mathbf{Q}^{-1} $$\n",
      "\n",
      "It's easy when you have all of the concepts lined up! For completeness, the covariance of the error is\n",
      "\n",
      "$$ \\mathbb{E}(\\hat{\\boldsymbol{\\beta}}-\\boldsymbol{\\beta}) (\\hat{\\boldsymbol{\\beta}}-\\boldsymbol{\\beta})^T\n",
      "= \\mathbb{E}(\\mathbf{K} \\boldsymbol{\\epsilon} \\boldsymbol{\\epsilon}^T \\mathbf{K}^T)=\\mathbf{K}\\mathbf{Q}\\mathbf{K}^T =(\\mathbf{W}^T \\mathbf{Q}^{-1} \\mathbf{W})^{-1}$$"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The following  simulation  illustrates these results."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from mpl_toolkits.mplot3d import proj3d\n",
      "from numpy.linalg import inv\n",
      "import matplotlib.pyplot as plt\n",
      "import numpy as np\n",
      "from numpy import matrix, linalg, ones, array\n",
      "\n",
      "Q = np.eye(3)*.1 # error covariance matrix\n",
      "#Q[0,0]=1\n",
      "\n",
      "beta = matrix(ones((2,1))) # this is what we are trying estimate\n",
      "W = matrix([[1,2],\n",
      "            [2,3],\n",
      "            [1,1]])\n",
      "\n",
      "ntrials = 50 \n",
      "epsilon = np.random.multivariate_normal((0,0,0),Q,ntrials).T \n",
      "y=W*beta+epsilon\n",
      "\n",
      "K=inv(W.T*inv(Q)*W)*matrix(W.T)*inv(Q) \n",
      "b=K*y #estimated beta from data\n",
      "\n",
      "fig = plt.figure()\n",
      "fig.set_size_inches([6,6])\n",
      "\n",
      "# some convenience definitions for plotting\n",
      "bb = array(b)\n",
      "bm = bb.mean(1)\n",
      "yy = array(y)\n",
      "ax = fig.add_subplot(111, projection='3d')\n",
      "\n",
      "ax.plot3D(yy[0,:],yy[1,:],yy[2,:],'mo',label='y',alpha=0.3)\n",
      "ax.plot3D([beta[0,0],0],[beta[1,0],0],[0,0],'r-',label=r'$\\beta$')\n",
      "ax.plot3D([bm[0],0],[bm[1],0],[0,0],'g-',lw=1,label=r'$\\hat{\\beta}_m$')\n",
      "ax.plot3D(bb[0,:],bb[1,:],0*bb[1,:],'.g',alpha=0.5,lw=3,label=r'$\\hat{\\beta}$')\n",
      "ax.legend(loc=0,fontsize=18)\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
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PSZQABvAwHogeERJv7/eWTqdpTrcaWbt2LdauXYs77rgDX/nKV5xejqUIgoBEIgGv12v5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\nBGOSTJgkEw9GP0hMWAyFlwqZlzCv1XqdtrIOWeszQOsAbus1UEdYDw7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       "text": [
        "<matplotlib.figure.Figure at 0x5f020d0>"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The figure above show the simulated $\\mathbf{y}$ data as magenta circles. The green dots show the corresponding estimates, $\\boldsymbol{\\hat{\\beta}}$ for each sample. The red and green lines show the true value of $\\boldsymbol{\\beta}$ versus the average of the estimated $\\boldsymbol{\\beta}$-values, $\\boldsymbol{\\hat{\\beta_m}}$. The matrix $\\mathbf{K}$ maps the magenta circles in the corresponding green dots. Note there are many possible ways to map the magenta circles to the plane, but the $\\mathbf{K}$ is the ones that minimizes the MSE for $\\boldsymbol{\\beta}$. \n",
      "\n",
      "The figure below shows more detail in the horizontal *xy*-plane above."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from  matplotlib.patches import Ellipse\n",
      "\n",
      "fig, ax = plt.subplots()\n",
      "fig.set_size_inches((6,6))\n",
      "ax.set_aspect(1)\n",
      "ax.plot(bb[0,:],bb[1,:],'g.')\n",
      "ax.plot([beta[0,0],0],[beta[1,0],0],'r--',label=r'$\\beta$',lw=4.)\n",
      "ax.plot([bm[0],0],[bm[1],0],'g-',lw=1,label=r'$\\hat{\\beta}_m$')\n",
      "ax.legend(loc=0,fontsize=18)\n",
      "ax.grid()\n",
      "\n",
      "bm_cov = inv(W.T*Q*W)\n",
      "U,S,V = linalg.svd(bm_cov) \n",
      "\n",
      "err = np.sqrt((matrix(bm))*(bm_cov)*(matrix(bm).T))\n",
      "theta = np.arccos(U[0,1])/np.pi*180\n",
      "\n",
      "ax.add_patch(Ellipse(bm,err*2/np.sqrt(S[0]),err*2/np.sqrt(S[1])\n",
      "                       ,angle=theta,color='pink',alpha=0.5))\n",
      "\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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cNtmHFq+574+PiB5/IFByJ9TBFAVkpgMJ/XiCV7kpha940zREM12euuLS4hkD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\n5E5onm+rvsWt70zFmo8ZfvB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       "text": [
        "<matplotlib.figure.Figure at 0x5cfe5f0>"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The figure above shows the green dots, which are individual estimates of $\\boldsymbol{\\hat{\\beta}}$ from the corresponding simulated $\\mathbf{y}$ data. The red dashed line is the true value for $\\boldsymbol{\\beta}$ and the green line ($\\boldsymbol{\\hat{\\beta_m}}$ ) is the average of all the green dots. Note there is hardly a visual difference between them. The pink ellipse provides some scale as to the covariance of the estimated $\\boldsymbol{\\beta}$  values. I invite you to download this [IPython notebook](https://github.com/unpingco/Python-for-Signal-Processing/blob/master/Gauss_Markov.ipynb)  and tweak the values for the error covariance or any other parameter. All of the plots should update and scale correctly.\n"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Summary"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The Gauss-Markov problem is the cornerstone of all Gaussian modeling and is thus one of the most powerful models used in signal processing. We showed how to estimate the unobservable parameters given noisy measurements using our previous work on projection. We also coded up a short example to illustrate how this works in a simulation. For a much more detailed approach, see Luenberger (1997).\n",
      "\n",
      "As usual, the corresponding IPython notebook for this post  is available for download [here](https://github.com/unpingco/Python-for-Signal-Processing/blob/master/Gauss_Markov.ipynb). \n",
      "\n",
      "Comments and corrections welcome!"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "References\n",
      "---------------\n",
      "\n",
      "* Luenberger, David G. *Optimization by vector space methods*. Wiley-Interscience, 1997."
     ]
    }
   ],
   "metadata": {}
  }
 ]
}